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1.assess health damage and risks caused by radiation exposure,
including low-dose exposures
2.assess health damage and risks caused by exposure to mutagenic
chemicals and cancer-causing toxins
3.reduce the likelihood of heritable mutations
Understanding the human genome will have an enormous impact on the
ability to assess risks posed to individuals by exposure to toxic
agents. Scientists know that genetic differences make some people more
susceptible and others more resistant to such agents. Far more work
must be done to determine the genetic basis of such variability. This
knowledge will directly address DOE's long-term mission to understand
the effects of low-level exposures to radiation and other
energy-related agents, especially in terms of cancer risk.

1.new energy sources (biofuels)
2.environmental monitoring to detect pollutants
3.protection from biological and chemical warfare
4.safe, efficient toxic waste cleanup
5.understanding disease vulnerabilities and revealing drug targets

In 1994, taking advantage of new capabilities developed by the genome
project, DOE initiated the Microbial Genome Program to sequence the
genomes of bacteria useful in energy production, environmental
remediation, toxic waste reduction, and industrial processing.
Despite our reliance on the inhabitants of the microbial world, we
know little of their number or their nature: estimates are that less
than 0.01% of all microbes have been cultivated and characterized.
Programs like the DOE Microbial Genome Program help lay a foundation
for knowledge that will ultimately benefit human health and the
environment. The economy will benefit from further industrial
applications of microbial capabilities.
Information gleaned from the characterization of complete genomes in
MGP will lead to insights into the development of such new
energy-related biotechnologies as photosynthetic systems, microbial
systems that function in extreme environments, and organisms that can
metabolize readily available renewable resources and waste material
with equal facility.
Expected benefits also include development of diverse new products,
processes, and test methods that will open the door to a cleaner
environment. Biomanufacturing will use nontoxic chemicals and enzymes
to reduce the cost and improve the efficiency of industrial processes.
Already, microbial enzymes are being used to bleach paper pulp, stone
wash denim, remove lipstick from glassware, break down starch in
brewing, and coagulate milk protein for cheese production. In the
health arena, microbial sequences may help researchers find new human
genes and shed light on the disease-producing properties of pathogens.
Microbial genomics will also help pharmaceutical researchers gain a
better understanding of how pathogenic microbes cause disease.
Sequencing these microbes will help reveal vulnerabilities and
identify new drug targets.
Gaining a deeper understanding of the microbial world also will
provide insights into the strategies and limits of life on this
planet. Data generated in this young program already have helped
scientists identify the minimum number of genes necessary for life and
confirm the existence of a third major kingdom of life. Additionally,
the new genetic techniques now allow us to establish more precisely
the diversity of microorganisms and identify those critical to
maintaining or restoring the function and integrity of large and small
ecosystems; this knowledge also can be useful in monitoring and
predicting environmental change. Finally, studies on microbial
communities provide models for understanding biological interactions
and evolutionary history.

Techniques aimed at crop improvement have been utilized for centuries.
Today, applied plant science has three overall goals: increased crop
yield, improved crop quality, and reduced production costs.
Biotechnology is proving its value in meeting these goals. Progress
has, however, been slower than with medical and other areas of
research. Because plants are genetically and physiologically more
complex than single-cell organisms such as bacteria and yeasts, the
necessary technologies are developing more slowly.

1.Improvements in Crop Yield and Quality

In one active area of plant research, scientists are exploring ways to
use genetic modification to confer desirable characteristics on food
crops. Similarly, agronomists are looking for ways to harden plants
against adverse environmental conditions such as soil salinity,
drought, alkaline earth metals, and anaerobic (lacking air) soil
conditions.
Genetic engineering methods to improve fruit and vegetable crop
characteristics - such as taste, texture, size, color, acidity or
sweetness, and ripening process, are being explored as a potentially
superior strategy to the traditional method of cross-breeding.
Research in this area of agricultural biotechnology is complicated by
the fact that many of a crop's traits are encoded not by one gene but
by many genes working together. Therefore, one must first identify all
of the genes that function as a set to express a particular property.
This knowledge can then be applied to altering the germlines of
commercially important food crops. For example, it will be possible to
transfer the genes regulating nutrient content from one variety of
tomatoes into a variety that naturally grows to a larger size.
Similarly, by modifying the genes that control ripening, agronomists
can provide supplies of seasonal fruits and vegetables for extended
periods of time.
Biotechnological methods for improving field crops, such as wheat,
corn and soybeans, are also being sought, since seeds serve both as a
source of nutrition for people and animals and as the material for
producing the next plant generation. By increasing the quality and
quantity of protein or varying the types in these crops, we can
improve their nutritional value.

The miraculous substance that contains all of our genetic
instructions, DNA, is rapidly becoming a key to modern medicine. By
focusing on the diaphanous and extraordinarily long filaments of DNA
that we inherit from our parents, scientists are finding the root
causes of dozens of previously mysterious diseases: abnormal genes.
These discoveries are allowing researchers to make precise diagnoses
and predictions, to design more effective drugs, and to prevent many
painful disorders. The new findings also pave the way for the
development of the ultimate therapy - substituting a normal gene for a
malfunctioning one so as to correct a patient's genetic defect
permanently.
Recently, scientists have made spectacular progress against two fatal
genetic diseases of children, cystic fibrosis and Duchenne muscular
dystrophy. In addition, they have identified the genetic flaws that
predispose people to more widespread, though still poorly understood
ailments - various forms of heart disease, breast and colon cancer,
diabetes, arthritis - which are not usually thought of as genetic in
origin.
While many of the researchers who are exploring our genetic wilderness
want to find the sources of the nearly 4,000 disorders caused by
defects in single genes, others have an even broader goal: They hope
to locate and map all of the 50,000 to 100,000 genes on our
chromosomes. This map of our complete biological inheritance "the
marvelous message, evolved for 3 billion years or more, which gives
rise to each one of us," as Robert Sinsheimer of the University of
California, Santa Barbara, calls it - will guide biological research
for years to come. And it will radically simplify the search for the
genetic flaws that cause disease.
Once scientists have identified such a flaw, they need to understand
just how it produces a particular illness. They must determine the
normal gene's function in human cells: What kind of protein does it
instruct the cells to make, in what quantities, at what times, and in
what specific places? Then the researchers can ask whether the genetic
flaw results in too little protein, the wrong kind of protein, or no
protein at all - and how best to counteract the effects of this
failure.
For most genetic disorders, researchers are still at the very
beginning of the trail. They have no clues to the DNA error that
causes a disease, and they are still trying to find large families
whose DNA patterns can help them track it down.
By contrast, scientists who work on cystic fibrosis and a few other
diseases have covered much of the trail. They have already succeeded
in correcting the gene defect inside living human cells by inserting
healthy genes into these cells in a laboratory dish - an achievement
that may lead to gene therapy.
The farther scientists go along the trail, the broader the
implications of their findings. For example, the discovery of the gene
defect that causes Duchenne muscular dystrophy, a muscle-wasting
disease, led scientists to identify a previously unknown protein that
plays an important role in all muscle function. This gives them a
clearer view of how muscle cells work and allows them to diagnose
other muscle disorders with exceptional precision, as well as devise
new approaches to treatment.
Any new treatment will need to be tested on animals. In fact, the next
explosion of information in medical genetics is expected to come from
the study of animals - particularly with defects that mimic human
disorders. The techniques for producing animal models of disease are
improving rapidly. Even today, "designer mice" are playing an
increasingly important role in research.
The growth of powerful computerized databases is bringing further
insights. Only a month after the discovery of the genetic error
involved in neurofibromatosis, a disfiguring and sometimes disabling
hereditary disease, a computer search revealed a match between the
protein made by normal copies of the newly uncovered gene and a
protein that acts to suppress the development of cancers of the lung,
liver, and brain - a key finding for cancer researchers.
Such revelations are becoming increasingly frequent. "If a new
sequence has no match in the databases as they are, a week later a
still newer sequence will match it," observes Walter Gilbert of
Harvard University.
Brain disorders such as schizophrenia or Alzheimer's disease may be
next to yield to the genetic approach. "We won't know what went wrong
in most cases of mental disease until we can find the gene that sets
it off," says James Watson, co-discoverer of the structure of DNA.

1.The Biological sequence/structure deficit:
At the beginning of 1998, in publicly available, non-redundant
databases, more than 3,00,000 protein sequences have been deposited,
and the number of partial sequences in public and proprietary
Expressed sequence tag databases is estimated to run into millions. By
contrast, the number of unique 3D structures in the Protein Data Bank
(PDB) was less than 1500. Although structural information is far more
complex to derive, store and manipulate than are sequence data, these
figures nevertheless highlight an enormous information deficit. This
situation is likely to get worse as the genome projects around the
world begin top bear fruit. Of course, the acquisition of structural
data is also hastening, and the future large-scale structure
determination enterprise could conceivably furnish 2000 3D structures
annually. But this is a small yield by comparison with that of
sequence databases, which are doubling in size every year, with a new
sequence being added, on average once a minute.

2.Biological Databases:
If we are to derive the maximum benefit from the deluge of sequence
information, we must deal with it in a concerted way; this means
establishing, maintaining and disseminating databases; providing easy
to use software to access the information they contain; and designing
state-of-the-art analysis tools to visualize and interpret the
structural and functional clues latent in the data.
The first, then, in analysing sequence information is to assemble it
into central shareable resources i.e. databases. Databases are
effectively electronic filling cabinets, a convenient and efficient
method of storing vast amounts of information. There are many
different database types, depending both on the nature of the
information being stored and on the manner of data storage( eg:
whether in flat-files, tables in a relational database or objects in
an object oriented database).
In the context of protein sequence analysis, we will encounter
primary, composite and secondary databases. Such resources store
different levels of information in totally different formats. In the
past, this has led to a variety of communication problems, but
emerging computer technologies are beginning to provide solutions,
allowing seamless, transparent access to disparate, distributed data
structures over the internet.
Primary and secondary databases are used to address different aspects
of sequence analysis, because they store different levels of protein
sequence information.
The primary structure of a protein is its amino acid sequence; these
are stored in primary databases as linear alphabets that denote the
constituent residues. The secondary structure of a protein corresponds
to regions of local regularity, which, in sequence alignments, are
often apparent as well conserved motifs; these are stored in secondary
databases as patterns. The tertiary structure of a protein arises from
the packing of its secondary structure elements which may form
discrete domains within a fold, or may give rise to autonomous folding
units or modules; complete folds, domains and modules are stored in
structure databases as sets of atomic co-ordinates.

3.Primary Sequence Databases

In the early 1980s, sequence information started to become more
abundant in the scientific literature. Realising this, several
laboratories saw that there might be advantages to harvesting and
storing these sequences in central repositories. Thus, several primary
database projects began to evolve in different parts of the world.

3.1 Nucleic acid Sequence Databases
The principle DNA sequence databases are GenBank (USA), EMBL (Europe)
and DDBJ (Japan), which exchange data on a daily basis to ensure
comprehensive coverage at each of the sites.
EMBL is the nucleotide sequence database from the European
Bioinformatics Institute. The rate of growth of DNA databases has been
following an exponential trend, with a doubling time less than a year.
EMBL data predominantly (more than 50%) consist of model organisms.
DNA Data Bank of Japan is produced, distributed and maintained by the
National Institute of Genetics.
GenBank, the DNA database from the National Center for Biotechnology
Information, exchanges data with both EMBL and DDBJ to help ensure
comprehensive coverage. The database is split into 17 smaller discrete
divisions.

3.2 Protein Sequence Databases
PIR, MIPS, SWISS-PROT, and TrEMBL are the major protein sequence databases.
PIR was developed for investigating evolutionary relations between
proteins. In its current form, the database is split into four
distinct sections PIR1-PIR4, which differ in terms of the quality of
data and the level of annotation provided.
MIPS collects and processes sequence data for the tripartite
PIR-International Protein sequence Database Project.
SWISS-PROT is a protein sequence database which, endeavors to provide
high level annotations, including descriptions of the function of the
protein, and of the structure of its domain, its post translational
modifications and so on.
TrEMBL was created as a supplement to the SWISS-PROT. It was designed
to address the need for a well structured SWISS-PROT-like resource
that would allow very rapid access to sequence data from the genome
projects, without having to compromise the quality of SWISS-PROT
itself by incorporating sequences with insufficient analysis and
annotation.

4. Composite Protein Sequences Databases
One solution to the problem of proliferation primary databases is to
compile a composite, i.e. a database that amalgamates a variety of
different primary sources. Composite databases render sequence
searching much more efficient, because they obviate the need to
interrogate multiple resources. The interrogation process is stream
lined still further if the composite has been designed to be
non-redundant, as this means that the same sequence need not be
searched more than once. The choices of different sources and the
application of different redundancy criteria have led to the emergence
of different composites. The major composite databases are
Non-Redundant Database, OWL, MIPSX, SWISS-PROT+TrMBL.

5.Secondary Databases
Secondary databases contain the fruits of analyses of the sequences
in the primary resources. Because there are several different primary
databases and a variety of ways of analysing protein sequences, the
information housed in each of the secondary resources is different.
Designing software tools that can search the different types of data,
interpret the range of outputs, and assess the biological significance
of the results is not a trivial task. SWISS-PROT has emerged as the
most popular primary source and many secondary databases now use it as
their basis.
Some of the main secondary resources are as follows:

Secondary database Primary source Stored Information
PROSITE SWISS-PROT Regular expressions
Profiles SWISS-PROT Weighted matrices
PRINTS OWL Aligned motifs
Pfam SWISS-PROT Hidden Marcov Models
BLOCKS PROSITE/PRINTS Aligned motifs (blocks)
IDENTIFY BLOCKS/PRINTS Fuzzy regular expressions

6.Tertiary Databases
Tertiary databases are the databases derived from information housed
in secondary (pattern) databases (e.g. the BLOCKS and eMOTIF
databases, which draw on the data stored within PROSITE and PRINTS).
The value of such resources is in providing a different scoring
perspective on the same underlying data, allowing the possibility to
diagnose relationships that might be missed using the original
implementation.

1. Nurture the training of scientists skilled in genomics research.
A number of approaches to training for genomics research should be
explored. These include providing fellowship and career awards and
encouraging the development of institutional training programs and
curricula. Training that will facilitate collaboration among
scientists from different disciplines, as well as courses that
introduce scientists to new technologies or approaches, should also be
included.

2. Encourage the establishment of academic career paths for genomic scientists.
Ultimately, a strong academic presence for genomic science is needed
to generate the training environment that will encourage individuals
to enter the field. Currently, the high demand for genome scientists
in industry threatens the retention of genome scientists in academia.
Attractive incentives must be developed to maintain the critical mass
essential for sponsoring the training of the next generation of genome
scientists.
c) Increase the number of scholars who are knowledgeable in both
genomic and genetic sciences and in ethics, law, or the social
sciences.
As the pace of genetic discoveries increases, the need for
individuals who have the necessary training to study the social impact
of these discoveries also increases. The ELSI program should expand
its efforts to provide postdoctoral and senior fellowship
opportunities for cross-training. Such opportunities should be
provided both to scientists and health professionals who wish to
obtain training in the social sciences and humanities and to scholars
trained in law, the social sciences, or the humanities who wish to
obtain training in genomic or genetic sciences.

The Human Genome Project has created the need for new kinds of
scientific specialists who can be creative at the interface of biology
and other disciplines, such as computer science, engineering,
mathematics, physics, chemistry, and the social sciences. As the
popularity of genomic research increases, the demand for these
specialists greatly exceeds the supply. In the past, the genome
project has benefited immensely from the talents of non-biological
scientists, and their participation in the future is likely to be even
more crucial. There is an urgent need to train more scientists in
interdisciplinary areas that can contribute to genomics. Programs must
be developed that will encourage training of both biological and
non-biological scientists for careers in genomics. Especially critical
is the shortage of individuals trained in Bioinformatics. Also needed
are scientists trained in the management skills required to lead large
data-production efforts. Another urgent need is for scholars who are
trained to undertake studies on the societal impact of genetic
discoveries. Such scholars should be knowledgeable in both
genome-related sciences and in the social sciences. Ultimately, a
stable academic environment for genomic science must be created so
that innovative research can be nurtured and training of new
individuals can be assured. The latter is the responsibility of the
academic sector, but funding agencies can encourage it through their
grants programs.

Bioinformatics support is essential to the implementation of genome
projects and for public access to their output. Bioinformatics needs
for the genome project fall into two broad areas: (i) databases and
(ii) development of analytical tools. Collection, analysis,
annotation, and storage of the ever increasing amounts of mapping,
sequencing, and expression data in publicly accessible, user-friendly
databases is critical to the project's success. In addition, the
community needs computational methods that will allow scientists to
extract, view, annotate, and analyze genomic information efficiently.
Thus, the genome project must continue to invest substantially in
these areas. Conservation of resources through development of portable
software should be encouraged.
a) Improve content and utility of databases. Databases are the
ultimate repository of genome project's data. As new kinds of data are
generated and new biological relationships discovered, databases must
provide for continuous and rapid expansion and adaptation to the
evolving needs of the scientific community. To encourage broad use,
databases should be responsive to a diverse range of users with
respect to data display, data deposition, data access, and data
analysis. Databases should be structured to allow the queries of
greatest interest to the community to be answered in a seamless way.
Communication among databases must be improved. Achieving this will
require standardization of nomenclature. A database of human genomic
information, analogous to the model organism databases and including
links to many types of phenotypic information, is needed.
b) Develop better tools for data generation, capture, and annotation.
Large-scale, high-throughput genomics centers need readily available,
transportable informatics tools for commonly performed tasks such as
sample tracking, process management, map generation, sequence
finishing, and primary annotation of data. Smaller users urgently need
reliable tools to meet their sequencing and sequence analysis needs.
Readily accessible information about the availability and utility of
various tools should be provided, as well as training in the use of
tools.
c) Develop and improve tools and databases for comprehensive
functional studies. Massive amounts of data on gene expression and
function will be generated in the near future. Databases that can
organize and display this data in useful ways need to be developed.
New statistical and mathematical methods are needed for analysis and
comparison of expression and function data, in a variety of cells and
tissues, at various times and under different conditions. Also needed
are tools for modeling complex networks and interactions.
d) Develop and improve tools for representing and analyzing sequence
similarity and variation. The study of sequence similarity and
variation within and among species will become an increasingly
important approach to biological problems. There will be many forms of
sequence variation, of which SNPs will be only one type. Tools need to
be created for capturing, displaying, and analyzing information about
sequence variation.
e) Create mechanisms to support effective approaches for producing
robust, exportable software that can be widely shared. Many useful
software products are being developed in both academia and industry
that could be of great benefit to the community. However, these tools
generally are not robust enough to make them easily exportable to
another laboratory. Mechanisms are needed for supporting the
validation and development of such tools into products that can be
readily shared and for providing training in the use of these
products. Participation by the private sector is strongly encouraged.

Functional genomics is the interpretation of the function of DNA
sequence on a genomic scale. Already, the availability of the sequence
of entire organisms has demonstrated that many genes and other
functional elements of the genome are discovered only when the full
DNA sequence is known. Such discoveries will accelerate as sequence
data accumulate. However, knowing the structure of a gene or other
element is only part of the answer. The next step is to elucidate
function, which results from the interaction of genomes with their
environment. Current methods for studying DNA function on a genomic
scale include comparison and analysis of sequence patterns directly to
infer function, large-scale analysis of the messenger RNA and protein
products of genes, and various approaches to gene disruption. In the
future, a host of novel strategies will be needed for elucidating
genomic function. This will be a challenge for all of biology. The HGP
will be contributing to this area by emphasizing the development of
technology that can be used on a large scale, is efficient, and is
capable of generating complete data for the genome as a whole. To the
extent that available resources allow, expansion of current approaches
as well as innovative technology ideas should be supported in the
areas described below.
a) Develop cDNA resources. Complete sets of full-length cDNA clones
and sequences for both humans and model organisms would be enormously
useful for biologists and are urgently needed. Such resources would
help in both gene discovery and functional analysis. High priority
should be placed on developing technology for obtaining full-length
cDNAs. Complete and validated inventories of full-length cDNA clones
and corresponding sequences should be generated and made available to
the community once such technology is at hand.
b) Improved technologies are needed for global approaches to the study
of non-protein-coding sequences, including production of relevant
libraries, comparative sequencing, and computational analysis.
c) Develop technology for comprehensive analysis of gene expression.
Information about the spatial and temporal patterns of gene expression
in both humans and model organisms offers one key to understanding
gene expression. Efficient and cost-effective technology needs to be
developed to measure various parameters of gene expression reliably
and reproducibly. Complementary DNA sequences and validated sets of
clones with unique identifiers will be needed for array technologies,
large-scale in situ hybridization, and other strategies for measuring
gene expression. Improved methods for quantifying, representing,
analyzing, and archiving expression data should also be developed.
d) Improve methods for genome-wide mutagenesis. Creating mutations
that cause loss or alteration of function is another prime approach to
studying gene function. Technologies, both gene- and phenotype-based,
which can be used on a large scale in vivo or in vitro, are needed for
generating or finding such mutations in all genes. Such technologies
should be piloted in appropriate model systems, including both cell
culture and whole organisms.
e) Develop technology for global protein analysis. A full
understanding of genome function requires an understanding of protein
function on a genome-wide basis. Development of experimental and
computational methods to study global spatial and temporal patterns of
protein expression, protein-ligand interactions, and protein
modification needs to be supported.

In the mid-1980s, the united states department of Energy initiated a
number of projects to construct detailed genetic and physical maps of
the human genome, to determine its complete nucleotide sequence, and
to localize its estimated 100000 genes. Work on this scale required
the development of new computational methods for analysing genetic map
and DNA sequence data, and demanded the design of new techniques and
instrumentation for detecting and analysing DNA. To benefit the public
most effectively, the projects also necessitated the use of advanced
means of information dissemination in order to make the results
available as rapidly as possible to scientists and physicians. The
international effort arising from this vast initiative became known as
the human genome project.
Similar research efforts were also launched to map and sequence the
genomes of a variety of organisms used extensively in research labs as
model systems. In April 1998, although the sequencing projects of only
a small number of relatively small genomes had been completed, and the
human genome is not expected to be complete until after the year 2003,
the results of such projects were already beginning to pour into the
public sequence databases in overwhelming numbers.

Goals for Advancements in Sequencing Technology
DNA sequencing technology has improved dramatically since the genome
projects began. The amount of sequence produced each year is
increasing steadily; individual centers are now producing tens of
millions of base pairs of sequence annually. In the future, de novo
sequencing of additional genomes, comparative sequencing of closely
related genomes, and sequencing to assess variation within genomes
will become increasingly indispensable tools for biological and
medical research. Much more efficient sequencing technology will be
needed than is currently available. The incremental improvements made
to date have not yet resulted in any fundamental paradigm shifts.
Nevertheless, the current state-of-the-art technology can still be
significantly improved, and resources should be invested to accomplish
this. Beyond that, research must be supported on new technologies that
will make even higher throughput DNA sequencing efficient, accurate,
and cost-effective, thus providing the foundation for other advanced
genomic analysis tools. Progress must be achieved in three areas:
a) Continue to increase the throughput and reduce the cost of current
sequencing technology.
Increased automation, miniaturization, and integration of the
approaches currently in use, together with incremental, evolutionary
improvements in all steps of the sequencing process, are needed to
yield further increases in throughput (to at least 500 Mb of finished
sequence per year by 2003) and reductions in cost. At least a twofold
cost reduction from current levels (which average $0.50 per base for
finished sequence in large-scale centers) should be achieved in the
next 5 years. Production of the working draft of the human sequence
will cost considerably less per base pair.
b) Support research on novel technologies that can lead to significant
improvements in sequencing technology.
New conceptual approaches to DNA sequencing must be supported to
attain substantial improvements over the current sequencing paradigm.
For example, microelectromechanical systems (MEMS) may allow
significant reduction of reagent use, increase in assay speed, and
true integration of sequencing functions. Rapid mass spectrometric
analysis methods are achieving impressive results in DNA fragment
identification and offer the potential for very rapid DNA sequencing.
Other more revolutionary approaches, such as single-molecule
sequencing methods, must be explored as well. Significant investment
in interdisciplinary research in instrumentation, combining chemistry,
physics, biology, computer science, and engineering, will be required
to meet this goal. Funding of far-sighted projects that may require 5
to 10 years to reach fruition will be essential. Ultimately,
technologies that could, for example, sequence one vertebrate genome
per year at affordable cost are highly desirable.
c) Develop effective methods for the advanced development and
introduction of new sequencing technologies into the sequencing
process.
As the scale of sequencing increases, the introduction of improvements
into the production stream becomes more challenging and costly. New
technology must therefore be robust and be carefully evaluated and
validated in a high-throughput environment before its implementation
in a production setting. A strong commitment from both the technology
developers and the technology users is essential in this process. It
must be recognized that the advanced development process will often
require significantly more funds than proof-of-principle studies.
Targeted funding allocations and dedicated review mechanisms are
needed for advanced technology development.
Developing Technology to handle Sequence Variations
Natural sequence variation is a fundamental property of all genomes.
Any two haploid human genomes show multiple sites and types of
polymorphism. Some of these have functional implications, whereas many
probably do not. The most common polymorphisms in the human genome are
single base-pair differences, also called single-nucleotide
polymorphisms (SNPs). When two haploid genomes are compared, SNPs
occur every kilobase, on average. Other kinds of sequence variation,
such as copy number changes, insertions, deletions, duplications, and
rearrangements also exist, but at low frequency and their distribution
is poorly understood. Basic information about the types, frequencies,
and distribution of polymorphisms in the human genome and in human
populations is critical for progress in human genetics. Better
high-throughput methods for using such information in the study of
human disease are also needed.
SNPs are abundant, stable, widely distributed across the genome, and
lend themselves to automated analysis on a very large scale, for
example, with DNA array technologies. Because of these properties,
SNPs will be a boon for mapping complex traits such as cancer,
diabetes, and mental illness. Dense maps of SNPs will make possible
genome-wide association studies, which are a powerful method for
identifying genes that make a small contribution to disease risk. In
some instances, such maps will also permit prediction of individual
differences in drug response. Publicly available maps of large numbers
of SNPs distributed across the whole genome, together with technology
for rapid, large-scale identification and scoring of SNPs, must be
developed to facilitate this research.
a) Develop technologies for rapid, large-scale identification of SNPs
and other DNA sequence variants. The study of sequence variation
requires efficient technologies that can be used on a large scale and
that can accomplish one or more of the following tasks: rapid
identification of many thousands of new SNPs in large numbers of
samples. Although the immediate emphasis is on SNPs, ultimately
technologies that can be applied to polymorphisms of any type must be
developed. Technologies are also needed that can rapidly compare, by
large-scale identification of similarities and differences, the DNA of
a species that is closely related to one whose DNA has already been
sequenced. The technologies that are developed should be
cost-effective and broadly accessible.
b) Identify common variants in the coding regions of the majority of
identified genes Initially, association studies involving complex
diseases will likely test a large series of candidate genes;
eventually, sequences in all genes may be systematically tested. SNPs
in coding sequences (also known as cSNPs) and the associated
regulatory regions will be immediately useful as specific markers for
disease. An effort should be made to identify such SNPs as soon as
possible. Ultimately, a catalog of all common variants in all genes
will be desirable. This should be cross-referenced with cDNA sequence
data.
c) Create an SNP map of at least 100,000 markers. A publicly available
SNP map of sufficient density and informativeness to allow effective
mapping in any population is the ultimate goal. A map of 100,000 SNPs
(one SNP per 30,000 nucleotides) is likely to be sufficient for
studies in some relatively homogeneous populations, while denser maps
may be required for studies in large, heterogeneous populations. Thus,
during this 5-year period, the HGP authorities have planned to create
a map of at least 100,000 SNPs. If technological advances permit, a
map of greater density is desirable. Research should be initiated to
estimate the number of SNPs needed in different populations.
d) Develop the intellectual foundations for studies of sequence
variation. The methods and concepts developed for the study of
single-gene disorders are not sufficient for the study of complex,
multigene traits. The study of the relationship between human DNA
sequence variation, phenotypic variation, and complex diseases depends
critically on better methods. Effective research design and analysis
of linkage, linkage disequilibrium, and association data are areas
that need new insights. Questions such as which study designs are
appropriate to which specific populations, and with which population
genetics characteristics, must be answered. Appropriate statistical
and computational tools and rigorous criteria for establishing and
confirming associations must also be developed.
e) Create public resources of DNA samples and cell lines. To
facilitate SNP discovery it is critical that common public resources
of DNA samples and cell lines be made available as rapidly as
possible. To maximize discovery of common variants in all human
populations, a resource is needed that includes individuals whose
ancestors derive from diverse geographic areas. It should encompass as
much of the diversity found in the human population as possible.
Samples in this initial public repository should be totally anonymous
to avoid concerns that arise with linked or identifiable samples.
DNA samples linked to phenotypic data and identified as to their
geographic and other origins will be needed to allow studies of the
frequency and distribution of DNA polymorphisms in specific
populations and their relevance to disease. However, such collections
raise many ethical, legal, and social concerns that must be addressed.
Credible scientific strategies must be developed before creating these
resources.

The tools of computer science, statistics and mathematics are
critical for studying biology as an informational science. Curiously,
biology is the only science that at it's very heart, employs a digital
language. The grand challenge in biology is to determine how the
digital language of the chromosomes is converted into 3-D and 4-D
(time varying) languages of living organisms.

Need for software automation

DNA encodes the information necessary for building and maintaining
life. DNA is a non-branching, double-stranded macromolecule in which
the nucleotide building blocks (A,C,G,T) are linked. Bases are
arranged in A-T and C-G pairs. Small viral genomes of the order of
several thousand bases were the first to be sequenced in 1970. Few
years later, genomes of the order of 40 kilo base pairs represented
the limit of what could reasonably be sequenced. At this stage, the
need for automation was recognized and methods were applied to the
degree possible. By the year 1997, the yeast genome consisting of 12
Mega base pairs was completed, and in 1998, the conclusion of the 100
Mega base pairs nematode genome project was announced. Most recently,
the 180 Mega base pairs fruit-fly genome was also completed. All of
these projects relied on substantially higher levels of software
automation. We are now in the midst of the most ambitious project so
far: sequencing of the 3 Giga base pairs Human Genome. For this
effort, and those yet to come, software automation lies at the very
core of the planning and executing of the project.
The need for automation is driven largely by the trend of handling
ever larger sizes of DNA and the corresponding increase in the amount
of raw data this entails. Mathematical analysis indicates that the
size of a project is roughly proportional to the size of the genome.
This is due to the fact that the amount of information obtained for an
individual sequencing experiment is relatively constant and is
independent of the genome size. It is estimated that for the human
genome, as order of 108 individual experiments are required to cover
the genome. To meet the projected goals, modern large scale sequencing
centers have developed throughput capacities of the order of several
million experiments per month, with data processing handled on a
continuous basis. Managing such large projects without a high degree
of automation would clearly be impossible in terms of cost and time
requirements.
So, DNA is the basic genetic material. It transmits hereditary
characters from one generation to the next. During synthesis of
proteins, mRNA which act as the messengers of information (the exact
genetic code) are build from DNA. Proteins are synthesized using mRNA
molecules. Protein interactions give rise to information pathways and
networks which help in building cells which are identical to their
parent cells. Clustering of many cells in a predefined format composes
a tissue. An organ is a combination of tissues and an organism is
nothing but an organization of organs. Refer figure 3.1.
The challenge for computer professionals is to create tools that can
capture and integrate these different levels of biological
information.

Genetic Algorithms
All that computers can do is implement algorithms. Hence when we talk
of using computers for processing of biological information, we have
to define precise mathematical algorithms. Following are a few
absolutely basic algorithms in Bioinformatics.

1.Database Searching
Database interrogation can take the form of text queries (e.g.
Display all the human adrenergic receptors) or sequence similarity
searches (e.g. Given the sequence of a human adrenergic receptor,
display all the similar sequences in the database). Sequence
similarity searches are straightforward because the data in the
databases is mostly in the form of sequences.

2Comparing Two Sequences
Let us take the case of comparing two protein sequences. The alphabet
complexity is 20, since a protein is nothing but a sequence of amino
acids and there are 20 possible amino acids. The naïve approach is to
line up the sequences against each other and insert additional
characters to bring the two strings into vertical alignment. More the
matches, more is the closeness in the two sequences.
The process of alignment can be measured in terms of the number of
gaps introduced and the number of mismatches remaining in the
alignment. A metric relating such parameters represents the distance
between two sequences.

3.Multiple Sequence Alignment
In the previous sub section, we saw pairwise sequence alignment,
which is fundamental to sequence analysis. However, analysis of groups
of sequences that form gene families requires the ability to make
connections between more than two members of the group, in order to
reveal subtle conserved family characteristics. The goal of multiple
sequence alignment is to generate a concise information-rich summary
of sequence data in order to inform decision-making on the relatedness
of sequences to a gene family.
Multiple sequence alignment is a 2D table, in which the rows
represent individual sequences and the columns the residue positions.
The sequences are laid onto this grid in such a manner that (a) the
relative positioning of residues within any one sequence is preserved,
and (b) similar residues in all the sequences are brought into
vertical register.

1.What is Bioinformatics?

Bioinformatics is the application of computers in biological sciences
and especially analysis of biological sequence data. It is concerned
with capturing, storing, graphically displaying, modeling and
ultimately distributing biological information. It is becoming an
essential tool in molecular biology as genome projects generate vast
quantities of data. With new sequences being added to DNA databases on
an average, once every minute, there is a pressing need to convert
this information into biochemical and biophysical knowledge by
deciphering the structural, functional and evolutionary clues encoded
in the language of biological sequences.

What Bioinformatics therefore offers to the researcher, the
entrepreneur, or the Venture Capitalist is an enormous and exciting
array of opportunities to discover how living systems metabolise,
grow, combat disease, reproduce and regenerate. The current knowledge
represents only the tip of the iceberg. Exciting and startling
discoveries are being made everyday through Bioinformatics, which is
building up an extensive encyclopedia from which life's mysteries will
be unraveled. The importance of computational science in collating
this information and its simultaneous interpretation by biologists is
the underlying ethos of Bioinformatics.

Having an interest in biology and having a strong inclination towards
genetics is all right. But from our point of view, the most important
thing is that biocomputing requires lots of software professionals.
And there is more to do for these people than the experts in biology.

2.Computers and Biology

Bioinformatics is the symbolic relationship between computational and
biological sciences. The ability to sort and extricate genetic codes
from a human genomic database of 3 billion base pairs of DNA in a
meaningful way is perhaps the simplest form of Bioinformatics. Moving
on to another level, Bioinformatics is useful in mapping different
people's genomes and deriving differences in their genetic make-up
through pattern recognition software. But that is the easiest part.
What is more complex is to decipher the genetic code itself to see
what the differences in genetic make-up between different people
translate into in terms of physiological traits. And there is yet
another level, which is even more intricate and that is the genetic
code itself. The genetic code actually codes for amino acids and
thereby proteins and the specific role, played by each of these
proteins controls the state of our health. The role or function of
each of our genes in coding for a specific protein, which in turn
regulates a particular metabolic pathway, is described as "functional
genomics". The true benefit of Bioinformatics therefore lies in
harnessing information pertaining to these genetic functions in order
to understand how human beings and other living systems operate.

Computational simulation of experimental biology is an important
application of Bioinformatics, which is referred to as "in silico"
testing. This is perhaps an area that will expand in a prolific way,
given the need to obtain a greater degree of predictability in animal
and human clinical trials. Added to this, is the interesting scope
that "in silico" testing provides to deal with the growing hostility
towards animal testing. The growth of this sector will largely depend
on the acceptance of "in silico" testing by the regulatory
authorities. However, irrespective of this, research strategies will
certainly find computational modeling to be a vital tool in speeding
up research with enormous cost benefits.

3.Limitations in the use of computers

The last decade has witnessed the dawn of a new era of
'silicon-based' biology, opening the door, for the first time, to the
possible investigation and comparative analysis of complete genomes.
Genome analysis means to elucidate and characterize the genes and gene
products of an organism. It depends on a number of pivotal concepts,
concerning the processes of evolution (divergence and convergence),
the mechanism of protein folding, and the manifestation of protein
function.

Today, our use of computers to model such processes is limited by,
and must be placed in the context of, the current limits of our
understanding of these central themes. At the outset, it is important
to recognize that we do not yet fully understand the rules of protein
folding; we cannot invariably say that a particular sequence or a fold
has arisen by divergent or convergent evolution; and we cannot
necessarily diagnose a protein function, given knowledge only of its
sequence or of its structure, in isolation. Accepting what we cannot
do with computers plays an essential role in forming an appreciation
of what, in fact, we can do. Without this kind of understanding, it is
easy to be misled, as spurious arguments are often used to promote
perhaps rather overenthusiastic points of view about what particular
programs and software packages can achieve.

Nature has its own complex rules, which we only poorly understand and
which we cannot easily encapsulate within computer programs. No
current algorithm can 'do' biology. Programs provide mathematical and
therefore infallible, models of biological systems. To interpret
correctly whether sequences or structures are meaningfully similar,
whether they have arisen by the processes of divergence or
convergence, whether similar sequences or similar folds have the same
or different functions: these are the most challenging problems. There
are no simple solutions, and computers do not give us the answers;
rather, given a sea of data, they help to narrow the options down so
that the users can begin to draw informed biologically reasonable
conclusions.

4.Current Stage of Research

In the field of Bioinformatics, the current research drive is to be
able to understand evolutionary relationships in terms of the
expression of protein function. Two computational approaches have been
brought up to bear on the problem, tackling the identification of
protein function from the perspectives of sequence analysis and of
structure analysis respectively. From the point of view of sequence
analysis, we are concerned with the detection of relationships between
newly determined sequences and those of known function (usually within
a database). This may mean pinpointing functional sites shared by
disparate proteins (probably the result of convergent evolution), or
identifying related functions in similar proteins (most commonly the
result of divergent evolution.

The identification of protein function from sequence sounds
straightforward, and indeed, sequence analysis is usually a fruitful
technique. But, function cannot be inferred from sequence for about
one-third of proteins in any of the sequenced genomes, largely because
biological characterization cannot keep pace with the volume of data
issuing from the genome projects (large number of database sequences
thus either carry no annotation beyond the parent gene name, or are
simply designated as hypothetical proteins). Another important point
is that, in some instances, closely related sequences, which may be
assumed to share a common structure, may not share the same function.
What this means is that, though sequence or structure analysis can be
used for deducing gene functions, still neither technique can be
applied infallibly without reference to the underlying biology.

5.Microbial, Plant and Animal Genomes

Although the human genome appears to be the focal point of interest,
microbial, plant and animal genomes are equally exciting to explore
through Bioinformatics. Mining plant genomics has an important impact
on opening up new vistas for research in agriculture. Microbial
genomics offers a dual opportunity of developing new
fermentation-based products and technologies as well as defining new
ways of combating microbial infections. Exploring animal genomics
opens up unlimited scope to pursue research in veterinary science and
transgenic models.

6.History (Stages of development)

The science of sequencing began slowly. The earliest techniques were
based on methods for separation of proteins and peptides, coupled with
methods for identification and quantification of amino acids. Prior
to 1945, there was not a single quantitative analysis available for
any one protein. However, significant progress with chromatographic
and labeling techniques over the next decade eventually led to the
elucidation of the first complete sequence, that of the peptide
hormone insulin (1955). Yet, it was the first five years before the
sequence of the first enzyme (ribonuclease) was complete (1960). By
1965, around 20 proteins with more than 100 residues had been
sequenced, and by 1980, the number was estimated to be of the order of
1500. Today, there are more than 3,00,000 sequences available.

Initially, the manual process of sequential Edman degradation –
dansylation, obtained the majority of protein sequences. A key step
towards the rapid increase in the number of sequenced proteins was the
development of automated sequencers, which, by 1980, offered a
104-fold increase in sensitivity relative to the automated procedure
implemented by Edman and Begg in 1967.

In the 1960s, scientists struggled to develop methods to sequence
nucleic acids, but the first techniques to emerge were really only
applicable to tRNA because they are short (74 to 95 nucleotides in
length) and it is possible to purify individual molecules.

As against RNA, DNA is very long: human chromosomal molecules may
contain between 55*106 and 250*106 base pairs. Assembling the complete
nucleotide sequence of a complete DNA molecule is a huge task. Even if
the sequence can be broken down into smaller fragments, purification
remains a problem. The advent of gene cloning provided a solution to
how the fragments can be separated. By 1977, two sequencing methods
had emerged using chain termination and chemical degradation
approaches. With only minor changes, the techniques propagated to
laboratories throughout the world, and laid the foundation for the
sequence revolution of the next two decades, and the subsequent birth
of Bioinformatics.

During the last decade, molecular biology has witnessed an
information revolution as a result of both of the development of rapid
DNA sequencing techniques and of the corresponding progress in
computer base technologies, which are allowing us to cope with this
information deluge in increasingly efficient ways. The broad term that
was coined in the mid-1980s to encompass computer applications in
biological sciences is Bioinformatics. The term Bioinformatics has
been commandeered by several different disciplines to mean rather
different things. In its broadest sense, the term can be considered to
mean information technology applied to the management and analysis of
biological sequence data; this has implications in diverse areas,
ranging from artificial intelligence and robotics to genome analysis.
In the context of genome initiatives, the term was originally applied
to the computational manipulation and analysis of biological sequence
data. However in view of this recent rapid accumulation of available
protein structures, the term now tends also to be used to embrace the
manipulation and analysis of 3D structure data.

This chapter explains in short some of the common biological terms
absolutely essential to get a clear understanding of what exactly is
Bioinformatics all about. I have avoided getting into the intricacies
of Genetics because the basic aim of this report is to know the latest
developments in the field of Bioinformatics, try to visualize where it
is heading, understand what it has got to offer to the community, and
exploit the opportunities available in this field.

2.1 Nucleotide

A nucleotide is a macromolecule made up of three sub-units: a pentose
sugar, a nitrogen base and a phosphate. Nucleic acids are polymers of
nucleotides. Pentose sugar is either ribose or deoxyribose (this
decides whether the genetic material formed is RNA or DNA). Nitrogen
bases are of two types: Purines (Adenine (A), Guanine (G)) and
Pyramidines (Cytosine (C), Thymine (T) and Uracil (U))

2.2 Amino acid

It is the fundamental building block of proteins. There are 20
naturally occurring amino acids in animals and around 100 more found
only in plants. A sequence of three nucleotides forms one amino acid.
The logic behind this is as follows: There are four types of
nucleotides depending on the nitrogenous base: (A,G,C,T) in DNA and
(A,G,C,U) in RNA. 20 different amino acids are to be coded using
permutations of 4 types of nucleotides. So obviously, 3 nucleotides
are required to signify one amino acid (43 > 20), because less than 3
will be insufficient and more than 3 will cause redundancy. The
sequence of three nucleotide specifying an amoni acid is called a
triplet code or codon (coding unit). All 64 codons specify something
or the other. Most of them specify amino acids, but a few are
instructions for starting and stopping the synthesis.

2.3 Properties of Genetic Code

1. Three nucleotides in a DNA molecule code for one amino acid in the
corresponding protein. Such a triplet is called a codon.
2. The code is read from a fixed starting point.
3. Codes for starting and stopping are present, but not for a pause in
the middle, or
comma.
4. The nucleotides are read three at a time in a non-overlapping manner.
5. Most of the 64 possible nucleotide triplets stand for one amino
acid or the other.
6. A few triplets stand for starting and stopping the synthesis.
7. There are two or more different codons for the same amino acid.
Because of this,
the genetic code is said to be degenerate.
8. The code has polarity because it can be read only in one direction.
9. The code is universal. Practically all the organisms use the same code.

2.4 DNA (Deoxy-ribonucleic Acid)

The long, thread-like DNA molecule consists of two strands that are
joined to one another all along their length. Each strand is a polymer
made up of repeated sub-units (nucleotides). Hence each strand is also
called a polynucleotide. DNA is the basic genetic material in all the
living material existing on this earth. The two essential mechanisms
possessed by DNA are (1) Transmission of hereditary characters and (2)
Ability of self-duplication. In the DNA molecule, tow long
polynucleotide chains are spirally twisted around each other. This is
also called helical coiling and the DNA is often referred to as a
double helix. A polynucleotide chain has polarity and the two strands
of a DNA molecule run in opposite directions, hence they are said to
be anti parallel. The two chains are joined together by hydrogen bonds
existing between the nitrogenous bases on the inside. Adenine (A)
forms a bond only with Thymine (T) and Guanine (G) can form a bond
only with Cytosine (C). Because of the base pairing restriction, the
two strands are always complementary to each other.
The sequence of bases along the polynucleotide is not restricted in
any way. An infinite variety of combinations is possible. It is the
precise sequence of bases that determines the genetic information.
There is no theoretical limit to the length of a DNA molecule.

2.5 Chromosomes
Chromosomes are the paired, self-replicating genetic structures of
cells that contain the cellular DNA; the nucleotide sequence of the
DNA encodes the linear array of genes.

2.6 Gene
A gene is the fundamental physical and functional unit of heredity. A
gene is an ordered sequence of nucleotides located in a particular
position on a particular chromosome that encodes a specific functional
product (i.e. a protein or RNA molecule).

2.7 Protein
Protein is a molecule composed of one or more chains of amino acids
in a specific order. The order is determined by the base sequence of
nucleotides in the gene coding for the protein. Proteins are required
for the structure, function and regulation of cells, tissues and
organs, each protein having a specific role (e.g., hormones, enzymes
and antibodies).
DNA carries the hereditary material and the only thing that they do
is to synthesize proteins, and thereafter, all the hereditary
characteristics get reflected in the activities of the body cells
because of proteins.

2.8 Sequencing
Sequencing means the determination of the order of nucleotides (base
sequences) in a DNA or RNA molecule, or the order of amino acids in a
protein.

2.9 Genome
Genome of an organism means all the genetic material in its
chromosomes. Its size is generally given as its total number of base
pairs. Genomes of different organisms can be compared to identify
similarities and disparities in the strategies for the 'Logic of
Life'.

2.10 Clone
Clone is an exact copy made of biological material such as a DNA
segment, a whole cell or a complete organism. The process of creating
a clone is called as cloning.

2.11 Model Organism

Saccharomytes cerevisiae commonly known as the baker's yeast have
emerged as the model organism. It has demonstrated the fundamental
conservation of the basic informational pathways found in almost all
the organisms. From the detailed study of the genomes of these
organisms (which is possible today), we can gain an insight into their
functioning. All this data will lead to the fundamental insights into
human biology.
Vast amount of genetic data available on this species provides
important clues helpful for the ongoing research on human genetics.
Saccharomytes cerevisiae has become the workhorse of many
biotechnology labs. It can exist either in a haploid or a diploid
state and divides by the vegetative process of budding. Yeast cultures
can be easily propagated in labs. It has become the model organism
partly because of the ease with which genetic manipulations can be
carried out. Random mutations can be induced into the genome by the
treatment of live cells with chemicals such as ethyl-methanesulphone
or by exposure to ultra-violet rays. Targeted gene inactivations can
also be carried out; this property is very important during
experiments for the unambiguous assignments of gene functions.
Saccharomytes cerevisiae has a compact genome of 12 lakh base pairs
of DNA present on 16 chromosomes. This presented a reasonable goal for
complete sequencing and analysis of it's genome. The Saccharomytes
genome database (SGD) was established at the Stanford University in
1995.
Knowing the complete sequence of a genome is only the first step in
understanding how the huge amount of information contained in genes is
translated into functional proteins.

Recent years have brought a continued increase in both the importance
of security in networked systems and the difficulty of securing them.
The Internet has continued to expand, its connections have become
nearly pervasive, and its protocols and services have grown more
complex. Beyond the basic need for integrity, confidentiality and
privacy, security has become essential toward providing reliability,
safety, and freedom from liability. One of the greatest threats to
security has come from automatic self-propagating attacks. These
attacks include both viruses and worms. While the presence of these
attacks is by no means new, the damage that they are able to inflict
and the speed with which they are able to propagate has become
paramount. Further increases in connectivity and complexity only
threaten to increase their virulence.
A computer worm is a self-replicating computer program,
similar to a computer virus. A virus attaches itself too, and becomes
part of, another executable program; however, a worm is self-contained
and does not need to be part of another program to propagate itself.
They are often designed to exploit the file transmission capabilities
found on many computers. The main difference between a computer virus
and a worm is that a virus can not propagate by itself whereas worms
can. A worm uses a network to send copies of it to other systems and
does so without any intervention. In general, worms harm the network
and consume bandwidth, whereas viruses infect or corrupt files on a
targeted computer. Viruses generally do not affect network
performance, as their malicious activities are mostly confined within
the target computer itself.
In addition to replication, a worm may be designed to do any
number of things, such as delete files on a host system or send
documents via e-mail. More recent worms may be multi-headed and carry
other executables as a payload. However, even in the absence of such a
payload, a worm can wreak havoc just with the network traffic
generated by its reproduction. Mydoom, for example, caused a
noticeable worldwide Internet slowdown at the peak of its spread. A
common payload is for a worm to install a backdoor in the infected
computer, as was done by Sobig and Mydoom. These zombie computers are
used by spam senders for sending junk email or to cloak their
website's address. Spammers are thought to pay for the creation of
such worms, and worm writers have been caught selling lists of IP
addresses of infected machines. Others try to blackmail companies with
threatened DoS attacks. The backdoors can also be exploited by other
worms, such as Doomjuice, which spreads using the backdoor opened by
Mydoom.

1.1 Typical worm spreading logic:
Most worms use random IP address generation for spreading to
different computers. The worm sends its code as an HTTP request to the
target computer. Then depending on the specific worm, it exploits the
known vulnerability in it. For example, the CodeRed worm sends a HTTP
request to exploit a buffer-overflow vulnerability, which allows the
worm to run on that computer. The malicious code is not saved as a
file, but is inserted into and then run directly from memory. There is
no particular strategy used by the different worms for intrusion. One
typical strategy used by W32-Blaster worm is given below.
It generates an IP address and attempts to infect the computer that
has that address. The IP address is generated according to the
following algorithms:
• For 40% of the time, the generated IP address is of the form
A.B.C.0, where A and B are equal to the first two parts of the
infected computer's IP address. Once the IP address is calculated, the
worm will attempt to find and exploit a computer with the IP address
A.B.C.0. The worm will then increment the 0 part of the IP address by
1, attempting to find and exploit other computers based on the new IP
address, until it reaches 254.
• With a probability of 60%, the generated IP address is completely random.
• To avoid looping back to infect the source computer, the worm will
not make HTTP requests to the IP addresses 127.*.*.*.
• Some fixed characteristics of the TCP and IP headers are:

1. IP identification = 256
2. Time to Live = 128
3. Source IP address = a.b.x.y, where a.b are from the host ip and x.y
are random. In some cases, a.b is random.
4. Destination IP address = dns resolution of "windowsupdate.com"
5. TCP Source port is between 1000 and 1999
6. TCP Destination port = 80
7. TCP Sequence number always has the two low bytes set to 0; the 2
high bytes are random.
8. TCP Window size = 16384

1.Human DNA Sequencing

Providing a complete, high-quality sequence of human genomic DNA to
the research community as a publicly available resource continues to
be the HGP's highest priority goal. The enormous value of the human
genome sequence to scientists, and the considerable savings in
research costs its widespread availability will allow, are compelling
arguments for advancing the timetable for completion. Recent
technological developments and experience with large-scale sequencing
provide increasing confidence that it will be possible to complete an
accurate, high-quality sequence of the human genome by the end of
2003, 2 years sooner than previously predicted. NIH and DOE expect to
contribute 60 to 70% of this sequence, with the remainder coming from
the effort at the Sanger Center and other international partners.
This is a highly ambitious goal, given that only about 6% of the human
genome sequence has been completed thus far. Sequence completion by
the end of 2003 is a major challenge, but within reach and well worth
the risks and effort. Realizing the goal will require an intense and
dedicated effort and a continuation and expansion of the collaborative
spirit of the international sequencing community. Only sequence of
high accuracy and long-range contiguity will allow a full
interpretation of all the information encoded in the human genome.
Availability of the human sequence will not end the need for
large-scale sequencing. Full interpretation of that sequence will
require much more sequence information from many other organisms, as
well as information about sequence variation in humans. Thus, the
development of sustainable, long-term sequencing capacity is a
critical objective of the HGP. Achieving the goals below will require
a capacity of at least 500 megabases (Mb) of finished sequence per
year by the end of 2003.

a) Finish the complete human genome sequence by the end of 2003.
To best meet the needs of the scientific community, the finished human
DNA sequence must be a faithful representation of the genome, with
high base-pair accuracy and long-range contiguity. Specific quality
standards that balance cost and utility have already been established.
These quality standards should be reexamined periodically; as
experience in using sequence data is gained, the appropriate standards
for sequence quality may change. One of the most important uses for
the human sequence will be comparison with other human and nonhuman
sequences. The sequence differences identified in such comparisons
should, in nearly all cases, reflect real biological differences
rather than errors or incomplete sequence. Consequently, the current
standard for accuracy--an error rate of no more than 1 base in
10,000--remains appropriate.
The current public sequencing strategy is based on mapped clones and
occurs in two phases. The first, or "shotgun" phase, involves random
determination of most of the sequence from a mapped clone of interest.
Methods for doing this are now highly automated and efficient. Mapped
shotgun data are assembled into a product ("working draft" sequence)
that covers most of the region of interest but may still contain gaps
and ambiguities. In the second, finishing phase, the gaps are filled
and discrepancies resolved. At present, the finishing phase is more
labor intensive than the shotgun phase. Already, partially finished,
working-draft sequence is accumulating in public databases at about
twice the rate of finished sequence.

b) Make the sequence totally and freely accessible.
The HGP was initiated because its proponents believed the human
sequence is such a precious scientific resource that it must be made
totally and publicly available to all who want to use it. Only the
wide availability of this unique resource will maximally stimulate the
research that will eventually improve human health.

2.Sequencing Technology

Create a long-term, sustainable sequencing capacity by improving
current technology and developing highly efficient novel technologies.
Achieving this HGP goal will require current sequencing capacity to be
expanded 2-3 times, demanding further incremental advances in standard
sequencing technologies and improvements in efficiency and cost. For
future sequencing applications, planners emphasize the importance of
supporting novel technologies that may be 5-10 years in development.

3.Sequence Variation

Develop technologies for rapid identification of DNA sequence
variants. A new priority for the HGP is examining regions of natural
variation that occur among genomes (except those of identical twins).
Goals specify development of methods to detect different types of
variation, particularly the most common type called single nucleotide
polymorphisms (SNPs) that occur about once every 1000 bases.
Scientists believe SNP maps will help them identify genes associated
with complex diseases such as cancer, diabetes, vascular disease, and
some forms of mental illness. These associations are difficult to make
using conventional gene hunting methods because any individual gene
may make only a small contribution to disease risk. DNA sequence
variations also underlie many individual differences in responses to
the environment and treatments.

4.Functional Genomics

Expand support for current approaches and innovative technologies.
Efficient interpretation of the functions of human genes and other DNA
sequences requires developing the resources and strategies to enable
large-scale investigations across whole genomes. A technically
challenging first priority is to generate complete sets of full-length
cDNA clones and sequences for human and model organism genes. Other
functional genomics goals include studies into gene expression and
control, creation of mutations that cause loss or alteration of
function in nonhuman organisms, and development of experimental and
computational methods for protein analyses.

5.Comparative Genomics

Obtain complete genomic sequences for C. elegans (1998), Drosophila
(2002), and mouse (2008). A first clue toward identifying and
understanding the functions of human genes or other DNA regions is
often obtained by studying their parallels in nonhuman genomes. To
enable efficient comparisons, complete genomic sequences already have
been obtained for the bacterium E. coli and the yeast S. cerevisiae,
and work continues on sequencing the genomes of the roundworm, fruit
fly, and mouse. Planners note that other genomes will need to be
sequenced to realize the full promise of comparative genomics,
stressing the need to build a sustainable sequencing capacity.

6.Ethical, Legal, and Social Implications (ELSI)

• Analyze and address implications of identifying DNA sequence
information for individuals, families, and communities.
• Facilitate safe and effective integration of genetic technologies.
• Facilitate education about genomics in nonclinical and research settings.

Rapid advances in genetics and applications present new and complex
ethical and policy issues for individuals and society. ELSI programs
that identify and address these implications have been an integral
part of the US HGP since its inception. These programs have resulted
in a body of work that promotes education and helps guide the conduct
of genetic research and the development of related health professional
and public policies. Continuing and new challenges include
safeguarding the privacy of individuals and groups who contribute
samples for large-scale sequence variation studies; anticipating how
resulting data may affect concepts of race and ethnicity; identifying
how genetic data could potentially be used in workplaces, schools, and
courts; commercial uses; and the impact of genetic advances on
concepts of humanity and personal responsibility.

7.Bioinformatics and Computational Biology

Improve current databases and develop new databases and better tools
for data generation and capture and comprehensive functional studies.
Continued investment in current and new databases and analytical tools
is critical to the success of the Human Genome Project and to the
future usefulness of the data. Databases must be structured to adapt
to the evolving needs of the scientific community and allow queries to
be answered easily. Planners suggest developing a human genome
database analogous to model organism databases with links to
phenotypic information. Also needed are databases and analytical tools
for the expanding body of gene expression and function data, for
modeling complex biological networks and interactions, and for
collecting and analyzing sequence variation data.

8.Training

Nurture the training of genomic scientists and establish career paths.
Increase the number of scholars knowledgeable in genomics and ethics,
law, or the social sciences. Planners note that future genomics
scientists will require training in interdisciplinary areas that
include biology, computer science, engineering, mathematics, physics,
and chemistry. Additionally, scientists with management skills will be
needed for leading large data-production efforts.

The fourth-generation mobile communication systems are expected to
become the infrastructure for the future, offering enhanced
convenience and economic performance for the use in life, as well as
higher reliability and security as compared to the third generation
mobile systems (IMT-2000) through the realization of faster
transmission speed, wider bandwidth communications and seamless
connections with other systems (e.g. mobile communication,
broadcasting)
This report documents the work carried out on the systems beyond third
generation mobile wireless networks. This is why it has been suitably
titled as 4G (fourth generation) mobile wireless networks. The aim of
this research line is to give the all over information for 4G mobile
wireless networks. An investigation, comparison, and evaluation of 1G,
2G, 2.5G, 3G and 4G systems, using a mobile terminal such as a cell
phone will be carried out.
Research has also been carried out in markets and applications,
network evolution and radio access for 4G. Initiatives have also been
identified that will have an impact on the development of 4G and how
it will diverge along with its concerns. The selection of multiple
access techniques suitable for 4G and 3G standards that will integrate
with 4G have also been addressed. Many enabling techniques including
software radio, smart antennas and digital signal processing aspects
are improving the spectral efficiency of 3G systems and have been
marked as suitable technologies for 4G.

What is Billy Goat System?
Billy Goat is a sensor designed system, built with the specific
purpose of detecting and identifying network service worms. Because of
this specific focus, Billy Goat can take advantage of worm specific
properties that would hinder general-purpose intrusion-detection
systems, toward more efficient and accurate detection.
What are the characteristics of Billy Goat System?
These requirements influence the desired characteristics of such a
system, particularly in the following aspects:
1. Accuracy: The goal of a WDS is the identification of worm-infected
machines. To offer real utility, it must be able to perform this task
with a high level of accuracy, so that its reports can be trusted by
system and network administrators as the basis for contention and
remediation action. A WDS can use highly-specialized techniques to
detect worm-infected machines. This enables increased accuracy, at the
expense of the ability to detect a wider range of attacks.
2. Speed: Given the explosive nature of modern worms, a WDS should be
able to detect an infected machine as quickly as possible, to provide
its users a chance to contain the damage, or even to function as the
basis for an automated response system.
3. Manageability: New worms and worm variants appear almost every day,
so the components of a WDS need to be updated regularly. At a systems
level, this process must be automated as much as possible, to be able
to deal with the monitoring of very large networks. At middleware and
architecture levels, this means the base infrastructure must offer
sufficient flexibility to enable the rapid creation of new detection
capabilities.
4. Interoperability: Many organizations suffer from the proliferation
of security tools, each with their own control, monitoring and
reporting mechanisms. Furthermore, many places already have some form
of monitoring console, virus-response policies and procedures, etc. A
WDS should integrate as much as possible with the existing tools and
processes.
5. Resilience: A WDS must operate under extreme conditions in terms of
network and processing load, particularly during worm outbreaks. These
conditions are more likely to induce failures than other environments.
However, a WDS has a specific advantage that is not enjoyed by most
other IDS's because of the repetitive nature of worm activity; the WDS
can afford to lose some data without reducing its utility. In
practice, this means it is satisfactory to build a system that can
"forcefully" recover from failure (for example, by automatically
rebooting or even reinstalling itself) rather than trying to resist
it.
6. Graceful degradation: While WDS's may benefit from a distributed
architecture, most worm outbreaks have the effect of overloading
network links. It is therefore necessary for all sensors to be able to
operate on their own (for example, reporting only local data). Given
this condition, while the global system may be impeded, its individual
sensors can still be useful during a worm outbreak.

A Final Word

Billy Goat has been designed to be scalable, to operate gracefully in
a large distributed environment, and to provide extremely accurate
detection of worm-infected machines. This paper describes a number of
interesting or useful techniques and components identified during the
process, of developing "Billy Goat".

Ajax, which stands for Asynchronous JavaScript and XML, is transforming the Web and Internet-based applications forever. The initial user interaction model of the Web is based on page-oriented HyperText Markup Language (HTML).

It is designed to display text and graphic information easily in a web browser. Its simple publication and viewing model has helped fuel the explosive adoption of the World Wide Web into the global phenomenon we know today.

In short, Ajax is redefining the Web at a rapid pace, thus requiring every organization with a Web presence or Internet-based business applications to develop a cohesive Ajax strategy.

FAQs:

Q.

Did Google invent Ajax? Did Adaptive Path help build Google's Ajax applications?

A.

Google did not invent Ajax. Google's recent products are simply the highest-profile examples of Ajax applications.

Q.

Is anyone selling Ajax components or trademarking the name? Where can I download it?

A.

Ajax isn't something we can download. It's an approach — a way of thinking about the architecture of web applications using certain technologies.

Q.

Is Ajax just another name for XMLHttpRequest?

A.

No. XMLHttpRequest is only part of the Ajax equation. XMLHttpRequest is the technical component that makes the asynchronous server communication possible; Ajax is our name for the overall approach described in the article, which relies not only on XMLHttpRequest, but on CSS, DOM, and other technologies.

Q.

Techniques for asynchronous server communication have been around for years. What makes Ajax a "new" approach?

A.

What's new is the prominent use of these techniques in real-world applications to change the fundamental interaction model of the Web. Ajax is taking hold now because these technologies and the industry's understanding of how to deploy them most effectively have taken time to develop.

Q.

Is Ajax a technology platform or is it an architectural style?

A.

It's both. Ajax is a set of technologies being used together in a particular way.

Q.

What kinds of applications is Ajax best suited for?

A.

We don't know yet. Because this is a relatively new approach, our understanding of where Ajax can best be applied is still in its infancy. Sometimes the traditional web application model is the most appropriate solution to a problem.

Q.

Does this mean Ajax is anti-Flash?

A.

Not at all. There have been supporters of Flash technology since years. As Ajax matures, we expect that sometimes Ajax will be the better solution to a particular problem, and sometimes Flash will be the better solution. We're also interested in exploring ways the technologies can be mixed (as in the case of Flickr.com, which uses both).

Q.

Does Ajax have significant accessibility or browser compatibility limitations? Do Ajax applications break the back button? Is Ajax compatible with REST? Are there security considerations with Ajax development? Can Ajax applications be made to work for users who have JavaScript turned off?

A.

The answer to all of these questions is "maybe". Many developers are already working on ways to address these concerns. We think there's more work to be done to determine all the limitations of Ajax, and we expect the Ajax development community to uncover more issues like these along the way.

Q.

Some of the Google examples we cite don't use XML at all. Do I have to use XML and/or XSLT in an Ajax application?

A.

No. XML is the most fully-developed means of getting data in and out of an Ajax client, but there's no reason we couldn't accomplish the same effects using a technology like JavaScript Object Notation or any similar means of structuring data for interchange.

Q.

Are Ajax applications easier to develop than traditional web applications?

A.

Not necessarily. Ajax applications inevitably involve running complex JavaScript code on the client. Making that complex code efficient and bug-free is not a task to be taken lightly, and better development tools and frameworks will be needed to help us meet that challenge.

Q.

Do Ajax applications always deliver a better experience than traditional web applications?

A.

Not necessarily. Ajax gives interaction designers more flexibility. However, the more power we have, the more caution we must use in exercising it. We must be careful to use Ajax to enhance the user experience of our applications, not degrade it.